Generative anomaly detection: a comprehensive review of modeling principles, advances, and future opportunities

The unprecedented evolution of generative artificial intelligence (GAI) has reshaped the landscape of anomaly detection, enabling models to capture complex data distributions and expose subtle deviations that are previously elusive to traditional methods. However, the rapid proliferation of generative techniques, ranging from autoencoders and generative adversarial networks to diffusion models and large pre-trained foundation models, has resulted in a fragmented body of knowledge lacking a unified understanding. This survey addresses this critical gap by presenting the first comprehensive synthesis of GAI-empowered anomaly detection. We propose a holistic taxonomy that unifies classical generative paradigms with the transformative role of foundation models, positioning them within the broader context of detection and representation learning. Beyond cataloging methods, we examine how complementary generative tasks such as anomaly synthesis and restoration enhance anomaly detection capabilities, and analyze their practical deployment across key application domains. We underscore the reciprocal relationship between advances in GAI and the evolving challenges of anomaly detection, further delineating unresolved issues and outlining promising research directions to stimulate future progress in the field. By consolidating disparate developments into a coherent framework and offering forward-looking insights, this survey seeks to catalyze deeper integration between the GAI and anomaly detection communities, advancing both theoretical foundations and practical impact in this rapidly evolving field. An online project for this survey is available at: https://github.com/zjiaqi725/Awesome-Generative-Anomaly-Detection.